Construction of an Artificially Intelligent Model for Accurate Detection of HCC by Integrating Clinical, Radiological, and Peripheral Immunological Features.

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Integrating comprehensive information on hepatocellular carcinoma (HCC) is essential to improve its early detection. We aimed to develop a model with multi-modal features (MMF) using artificial intelligence (AI) approaches to enhance the performance of HCC detection. A total of 1,092 participants were enrolled from 16 centers. These participants were allocated into the training, internal validation, and external validation cohorts. Peripheral blood specimens were collected prospectively and subjected to mass cytometry analysis. Clinical and radiological data were obtained from electrical medical records. Various AI methods were employed to identify pertinent features and construct single-modal models with optimal performance. The XGBoost algorithm was utilized to amalgamate these models, integrating multi-modal information and facilitating the development of a fusion model. Model evaluation and interpretability were demonstrated using the SHapley Additive exPlanations method. We constructed the electronic health record, BioScore, RadiomicScore, and DLScore models based on clinical, radiological, and peripheral immunological features, respectively. Subsequently, these single-modal models were amalgamated to develop an all-in-one MMF model. The MMF model exhibited enhanced performance compared to models comprising only single-modal features in detecting HCC. This superiority in performance was confirmed through the internal and external validation cohorts, yielding area under the curve (AUC) values of 0.985 and 0.915, respectively. Additionally, the MMF model improved the detection ability in subpopulations of HCCs that were negative for alpha-fetoprotein and those with small size, with AUC values of 0.974 and 0.996, respectively. Integrating multi-modal features improved the performance of the model for HCC detection.